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   "source": [
    "## Gauss Newton Algorithm \n",
    "\n",
    "The Gauss–Newton algorithm is used to solve non-linear least squares problems. It is a modification of Newton's method for finding a minimum of a function. Unlike Newton's method, the Gauss–Newton algorithm can only be used to minimize a sum of squared function values, but it has the advantage that second derivatives, which can be challenging to compute, are not required.\n",
    "\n",
    "Non-linear least squares problems arise for instance in non-linear regression, where parameters in a model are sought such that the model is in good agreement with available observations.\n",
    "\n",
    "We use Gauss-Newton algorithm to minimize the cost function. The cost function in our case is the difference between the known 'gravitational and earth's magnetic vectors' and the (sensors accelerometer and magnetometer) readings transformed to the Earth frame. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the data from the sensors for acceleration and magnetic vectors as observation vectors. They are symbolized as \n",
    "\n",
    "\\begin{equation}\n",
    "A_{Device} = (a_{xb}, a_{yb}, a_{zb})\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "M_{Device} = (m_{xb}, m_{yb}, m_{zb})\n",
    "\\end{equation}\n",
    "\n",
    "In the Earth's frame of reference, the acceleration and magnetic vector represetation is \n",
    "\\begin{equation}\n",
    "A_{Earth} = (0, g, 0)\n",
    "\\end{equation}\n",
    "\n",
    "Where, $g$ is Earth's gravitational acceleration equal to $9.8 m/s^2$\n",
    "\\begin{equation}\n",
    "M_{Earth} = (m_{xE}, m_{yE}, m_{zE})\n",
    "\\end{equation}\n",
    "\n",
    "By combining the vecotrs in each of the references we get \n",
    "\\begin{equation}\n",
    "Y_{Earth} = (0, g, 0, m_{xE}, m_{yE}, m_{zE})\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "Y_{device} = (a_{xb}, a_{yb}, a_{zb}, m_{xb}, m_{yb}, m_{zb})\n",
    "\\end{equation}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rotation matrix that shall rotate the device vector to the Earth vector, in quaternion form is : \n",
    "\n",
    "\n",
    "$$\\mathbf{R_{t}} = \\left[\\begin{array}\n",
    "{rrr}\n",
    "M_{t} & 0 \\\\\n",
    "0 & M_{t} \n",
    "\\end{array}\\right]\n",
    "$$\n",
    "\n",
    "Where the matrix M is described as : \n",
    "$$\\mathbf{M_{t}} = \\left[\\begin{array}\n",
    "{rrr}\n",
    "q_4^2+q_1^2-q_2^2-q_3^2 & 2(q_1q_2-q_3q_4) & 2(q_1q_3 + q_2q_4) \\\\\n",
    "2(q_1q_2 + q_3q_4) & q_4^2+q_2^2-q_1^2-q_3^2 & 2(q_2q_3-q_4q_1)\\\\\n",
    "2(q_1q_3 - q_2q_4) & 2(q_3q_2+q_1q_4) & q_4^2+q_3^2-q_1^2-q_2^2)\n",
    "\\end{array}\\right]\n",
    "$$\n",
    "\n",
    "The Gauss-Newton optimization method is used to minimize discrepancy between\n",
    "actual and computed measurement vectors as below equation : \n",
    "\n",
    "\\begin{equation}\n",
    "\\epsilon  = Y_{Earth} - R_{t}.Y_{Device}\n",
    "\\end{equation}\n",
    "\n",
    "The Gauss-Newton method executes following iteration:\n",
    "\n",
    "\\begin{equation}\n",
    " q_t = q_{t-1} - (J_{t}^T.J_{t})^{-1}.J_{t}^T.\\epsilon\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "Where $J_k$ is the Jacobian of $\\epsilon$ calculated in $q_k$ as is shown in below equation\n",
    "\\begin{equation}\n",
    " J_{t}(q_{k}(t)) = \\frac{\\partial \\epsilon}{\\partial (q_{k}(t))} = - [ (\\frac{\\partial R}{\\partial (q_{1})} . Y_{Device(t)})(\\frac{\\partial R}{\\partial (q_{2})} . Y_{Device(t)})((\\frac{\\partial R}{\\partial (q_{3})} . Y_{Device(t)}))((\\frac{\\partial R}{\\partial (q_{4})} . Y_{Device(t)}))  ]  \n",
    "\\end{equation}\n",
    " \n",
    "and solving Jacobian.\n"
   ]
  },
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    "This is the New cell added."
   ]
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   "source": [
    "import math\n",
    "import numpy as np\n",
    "from numpy import linalg as la\n",
    "\n",
    "class GaussNewtonOptimization:\n",
    "    \"\"\"\n",
    "    The Gauss–Newton algorithm is used to solve non-linear least squares problems. \n",
    "    \"\"\"\n",
    "    ## class variables : to be used later \n",
    "    counter = 0\n",
    "    depth = 20\n",
    "    \n",
    "    def __call__(self):\n",
    "        GaussNewtonOptimization.counter += 1\n",
    "        return GaussNewtonOptimization.counter\n",
    "    \n",
    "    def __init__(self, Q1,Q2,Q3,Q4,Ax,Ay,Az,Mx,My,Mz):\n",
    "        self.Q1 = Q1\n",
    "        self.Q2 = Q2\n",
    "        self.Q3 = Q3\n",
    "        self.Q4 = Q4\n",
    "        \n",
    "        self.Ax = Ax\n",
    "        self.Ay = Ay\n",
    "        self.Az = Az\n",
    "        \n",
    "        self.Mx = Mx\n",
    "        self.My = My\n",
    "        self.Mz = Mz\n",
    "        \n",
    "        ## added changes considering equation 4.67 from page 60\n",
    "        self.Q_now = list([self.Q1,self.Q2,self.Q3,self.Q4])\n",
    "\n",
    "    \n",
    "    def norm(Q):\n",
    "        \"\"\"\n",
    "        return the norm of the Quaternion.\n",
    "        \"\"\"\n",
    "        return sum( i*i for i in Q)\n",
    "\n",
    "    def Gaussnewton(self):\n",
    "        \"\"\"\n",
    "        Gauss Newton Algorithm : returns the Quaternion derived from Accelerometer and Magnetometer,\n",
    "        iterated 10 times. \n",
    "        \"\"\"\n",
    "        Q1 = self.Q1\n",
    "        Q2 = self.Q2 \n",
    "        Q3 = self.Q3 \n",
    "        Q4 = self.Q4 \n",
    "        \n",
    "        Ax = self.Ax \n",
    "        Ay = self.Ay \n",
    "        Az = self.Az \n",
    "        \n",
    "        Mx = self.Mx \n",
    "        My = self.My\n",
    "        Mz = self.Mz \n",
    "        \n",
    "        # reference Attitude\n",
    "        EarthFrame = np.matrix([[0], [1] , [0], [0], [-0.03751], [0.92696]])\n",
    "        BodyFrame = np.matrix([[Ax], [Ay] , [Az], [Mx], [My], [Mz]])\n",
    "        \n",
    "        \n",
    "        Q_now = list([Q1,Q2,Q3,Q4])\n",
    "        #self.Q_now\n",
    "        #normalize\n",
    "        Q_now = [x / GaussNewtonOptimization.norm(Q_now) for x in Q_now]   \n",
    "               \n",
    "        q1 = Q_now[0] ; q2 = Q_now[1] ; q3 = Q_now[2] ; q4 = Q_now[3] \n",
    "        #from list to a column matrix\n",
    "        Q_now = np.matrix(Q_now).transpose()\n",
    "        #temp = Q_now\n",
    "        intermediateQ = list()\n",
    "        intermediateQ.append(Q_now)\n",
    "        \n",
    "        \n",
    "        for i in range(0,GaussNewtonOptimization.depth):\n",
    "            ## Compute Mt matrix\n",
    "\n",
    "            # The rotation matrix that rotates the \"body vector\" to the \"Earth vector\", in Quaternion form, \n",
    "            # is expressed in equation XX of the article; which has Mt matrix in it. \n",
    "\n",
    "            ## elements of Mt matrix \n",
    "            mm11 = (q4**2+q1**2-q2**2-q3**2)  ;   mm12 = 2*(q1*q2-q3*q4)           ;  mm13 = 2*(q1*q3+q2*q4)\n",
    "            mm21 = 2*(q1*q2+q3*q4)            ;   mm22 = (q4**2+q2**2-q1**2-q3**2) ;  mm23 = 2*(q2*q3-q4*q1)\n",
    "            mm31 = 2*(q1*q3-q2*q4)            ;   mm32 = 2*(q3*q2+q1*q4)           ;  mm33 = (q4**2+q3**2-q1**2-q2**2)  \n",
    "\n",
    "            Mt = np.matrix([[mm11, mm12, mm13], [mm21, mm22, mm23] , [mm31, mm32, mm33]])\n",
    "            zero33 = np.matrix(np.zeros((3,3)))  \n",
    "\n",
    "            ## Rotation Matrix (6 * 6)\n",
    "            Rt = np.hstack((np.vstack((Mt,zero33)),np.vstack((zero33,Mt))))\n",
    "\n",
    "            ## Jacobian Computation \n",
    "\n",
    "            j11 = 2*(q1*Ax + q2*Ay + q3*Az)\n",
    "            j12 = 2*(-q2*Ax +  q1*Ay + q4*Az) \n",
    "            j13 = 2*(q3*Ax - q4*Ay + q1*Az)\n",
    "            j14 = 2*(q4*Ax - q3*Ay + q2*Az)\n",
    "\n",
    "            j21 = 2*(q2*Ax - q1*Ay - q4*Az)\n",
    "            j22 = 2*(q1*Ax - q1*Ay - q4*Az)\n",
    "            j23 = 2*(q4*Ax - q3*Ay + q2*Az)\n",
    "            j24 = 2*(q3*Ax + q4*Ay - q1*Az)\n",
    "\n",
    "            j31 = 2*(q3*Ax + q4*Ay - q1*Az)\n",
    "            j32 = 2*(-q4*Ax + q3*Ay - q2*Az)\n",
    "            j33 = 2*(q1*Ax + q2*Ay + q3*Az)\n",
    "            j34 = 2*(q2*Ax + q1*Ay + q4*Az)\n",
    "\n",
    "            j41 = 2*(q1*Mx + q2*My + q3*Mz)\n",
    "            j42 = 2*(-q2*Mx + q1*My + q4*Mz)\n",
    "            j43 = 2*(-q3*Mx - q4*My + q1*Mz)\n",
    "            j44 = 2*(q4*Mx - q3*My + q2*Mz)\n",
    "\n",
    "            j51 = 2*(q2*Mx - q1*My - q4*Mz)\n",
    "            j52 = 2*(q1*Mx + q2*My + q3*Mz)\n",
    "            j53 = 2*(q4*Mx - q3*My + q2*Mz)\n",
    "            j54 = 2*(q3*Mx + q4*My - q1*Mz)\n",
    "\n",
    "            j61 = 2*(q3*Mx + q4*My - q1*Mz)\n",
    "            j62 = 2*(-q4*Mx + q3*My - q2*Mz)\n",
    "            j63 = 2*(q1*Mx + q2*My + q3*Mz)\n",
    "            j64 = 2*(-q2*Mx + q1*My + q4*Mz)\n",
    "\n",
    "            Mt = np.matrix([[mm11, mm12, mm13], [mm21, mm22, mm23] , [mm31, mm32, mm33]])\n",
    "            Jacobian_matrix = -np.matrix([[j11,j12,j13,j14],\n",
    "                                         [j21,j22,j23,j24],\n",
    "                                         [j31,j32,j33,j34],\n",
    "                                         [j41,j42,j43,j44],\n",
    "                                         [j51,j52,j53,j54],\n",
    "                                         [j61,j62,j63,j64],                                  \n",
    "                                        ])\n",
    "\n",
    "            f = EarthFrame - (Rt * BodyFrame)\n",
    "            \n",
    "            \n",
    "            ## Gauss Newton\n",
    "            Q_next = Q_now - (la.inv(Jacobian_matrix.transpose()*Jacobian_matrix ) ) * (Jacobian_matrix.transpose() * f)\n",
    "            \n",
    "            #normalize (do not renormalise in Kalman filter)\n",
    "            Q_next = [x / GaussNewtonOptimization.norm(np.array(Q_next).flatten()) for x in np.array(Q_next).flatten()]  \n",
    "            \n",
    "            \n",
    "            \n",
    "            # update quaternions\n",
    "            q1 = Q_next[0] ; q2 = Q_next[1] ; q3 = Q_next[2] ; q4 = Q_next[3] \n",
    "            Q_now = np.matrix(Q_next).transpose()\n",
    "            intermediateQ.append(Q_now)\n",
    "            #End of 'for' loop\n",
    "            \n",
    "        q1 = Q_next[0] ; q2 = Q_next[1] ; q3 = Q_next[2] ; q4 = Q_next[3]\n",
    "        return intermediateQ\n",
    "        #return q1,q2,q3,q4\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Execute the Gauss Newton Algorithm\n",
    "\n",
    "### Import the data \n",
    "\n",
    "We import the data from a text file, and save it as a Pandas dataframe. Add column Names to it. \n",
    "\n",
    "1. Accelerometer  : $A_{x},A_{y},A_{z}$ \n",
    "2. Magnetometer   : $MF_x,MF_y,MF_z$\n",
    "\n",
    "will be the columns of interest for us. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "\n",
    "#HIMU-2017-03-21_15-58-07 ; HIMU-2017-04-17_16-43-40 ; At0degrees.txt\n",
    "dataraw = pd.read_csv(\"/home/omkar/thesis/Version4/rawdata/AtX50degrees.txt\", skiprows=4)\n",
    "#/home/omkar/thesis/Version4/rawdata/IdlesetAngle.txt\n",
    "#dataraw = pd.read_csv(\"/home/omkar/thesis/Version4/rawdata/test2.txt\", skiprows=4)\n",
    "\n",
    "dataraw.shape\n",
    "## Data Extraction\n",
    "dataraw.columns = ['Timestamp','STK3310_Proximity_sensor_x','STK3310_Proximity_sensor_y','STK3310_Proximity_sensor_z', \n",
    "             'STK3310_Light_sensor_x','STK3310_Light_sensor_y','STK3310_Light_sensor_z',\n",
    "             'Display_Rotation_sensor_x','Display_Rotation_sensor_y','Display_Rotation_sensor_z',\n",
    "             'Ax','Ay','Az',\n",
    "             'MFx','MFy','MFz', \n",
    "             'Gx','Gy','Gz',\n",
    "             'Rotation_Vector_Sensor_x','Rotation_Vector_Sensor_y','Rotation_Vector_Sensor_z',\n",
    "             'Gravity_Sensor_x','Gravity_Sensor_y','Gravity_Sensor_z',\n",
    "             'Linear_Acceleration_Sensor_x','Linear_Acceleration_Sensor_y','Linear_Acceleration_Sensor_z',\n",
    "             'Orientation_Sensor_x','Orientation_Sensor_y','Orientation_Sensor_z']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we consider only the data with accelerometer, gyroscope and magnetometer readings along with timestamp. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "data = dataraw[['Timestamp','Ax','Ay','Az','Gx','Gy','Gz','MFx','MFy','MFz']]\n",
    "data.head(2)\n",
    "data[\"AxC\"] = data[\"Ax\"]/9.8\n",
    "data[\"AyC\"] = data[\"Ay\"]/9.8\n",
    "data[\"AzC\"] = data[\"Az\"]/9.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "       Timestamp        Ax        Ay        Az   Gx   Gy   Gz  MFx  MFy  \\\n",
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       "\n",
       "     MFz       AxC       AyC       AzC  \n",
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    "data.head(2)"
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   "execution_count": 25,
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8/cgtymX1wdX0aNyD2COxTN44mRaBLUjNTSX2SCzg+u2eMGACdXzq8OmOT5my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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6226fce080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4//77eeSRR1i0aBHLly/n/e9/P7feeisAF198MU1NTXzwgx+c5JaqW6lMVaocioiIiMgE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\nIiIiIiLA/wcX/orXn7AnqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6227205630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uv7K98ffDp9fKMpFd5X9ACHQY7AkIDyyX9GjqEw3v+hk3pvpzvwBpSYno4kkP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PtenJV2Fn8f+Pdiafvz9zPwzYHBOzLdmnTj1UGvHrLtzgM7GfCG9yXYq3mvYHv/Pbl7uPCtC9mf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\n3/eZWVm7v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f622719bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the Sensor Values \n",
    "data.plot(x='Timestamp',y=['AxC','AyC','AzC'],title=\"Acceleromter Training Raw Data\",figsize=(15.0, 4.0))\n",
    "data.plot(x='Timestamp',y=['Gx','Gy','Gz'],title=\"Gyroscope Training Raw Data\",figsize=(15.0, 4.0))\n",
    "data.plot(x='Timestamp',y=['MFx','MFy','MFz'],title=\"Magnetometer Training Raw Data\",figsize=(15.0, 4.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#?data[\"Pitch\"] = \n",
    "#?np.arctan()\n",
    "\n",
    "data[\"Pitch\"] = np.arctan(data[\"AxC\"] / (data[\"AxC\"]*data[\"AxC\"] + data[\"AzC\"]*data[\"AzC\"]))\n",
    "\n",
    "data[\"Roll\"] = np.arctan(data[\"AyC\"] / (data[\"AyC\"]*data[\"AyC\"] + data[\"AzC\"]*data[\"AzC\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp    1.494780e+12\n",
       "Ax           3.498182e-01\n",
       "Ay          -1.278746e-01\n",
       "Az           9.645478e+00\n",
       "Gx          -1.081219e-03\n",
       "Gy           3.489109e-03\n",
       "Gz           2.149552e-03\n",
       "MFx         -8.154090e+00\n",
       "MFy          6.280098e+00\n",
       "MFz         -3.016979e+01\n",
       "AxC          3.569574e-02\n",
       "AyC         -1.304843e-02\n",
       "AzC          9.842324e-01\n",
       "Pitch        3.689065e-02\n",
       "Roll        -1.347054e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TowgOMtQ0OwkykF/dwtbiemaMjGNSSiwx4fsPYVnF9ThcHuaMjmdLUR0hQUHM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\nq8kdRzMzMzMzM6vpv5+ex2dKyGnoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6226f44860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Timestamp',y=['Pitch','Roll'],title=\"Pitch\",figsize=(15.0, 4.0))\n",
    "data.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$\\epsilon  = Y_{Earth} - R_{t}.Y_{Device}$\n",
    "\n",
    "The Gauss-Newton method executes following iteration:\n",
    "\n",
    "$ q_t = q_{t-1} - (J_{t}^T.J_{t})^{-1}.J_{t}^T.\\epsilon$\n",
    "\n",
    "To represent the $q_{t}\\;at\\;t=0$ we have $Q_{observation} = [0.5,0.5,0.5,0.5]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[matrix([[ 0.5],\n",
       "         [ 0.5],\n",
       "         [ 0.5],\n",
       "         [ 0.5]]), matrix([[-0.02820665],\n",
       "         [ 1.05836147],\n",
       "         [ 0.36449057],\n",
       "         [ 1.09633797]]), matrix([[ 0.4680129 ],\n",
       "         [ 0.17165548],\n",
       "         [ 0.31273512],\n",
       "         [ 0.15186517]]), matrix([[ 2.6411859 ],\n",
       "         [ 0.85755231],\n",
       "         [ 1.48416085],\n",
       "         [ 0.63456401]]), matrix([[ 0.42928763],\n",
       "         [ 0.25279059],\n",
       "         [ 0.27437116],\n",
       "         [ 0.20314927]]), matrix([[ 2.14591827],\n",
       "         [ 1.61152039],\n",
       "         [ 1.26559017],\n",
       "         [ 1.18306038]]), matrix([[ 0.11584358],\n",
       "         [ 0.39191363],\n",
       "         [ 0.14662005],\n",
       "         [ 0.29444086]]), matrix([[ 2.09173734],\n",
       "         [ 1.66204574],\n",
       "         [ 1.07202248],\n",
       "         [ 1.02503761]]), matrix([[ 0.12602058],\n",
       "         [ 0.43397417],\n",
       "         [ 0.14703817],\n",
       "         [ 0.27904281]]), matrix([[ 2.0626165 ],\n",
       "         [ 1.33981079],\n",
       "         [ 0.83836332],\n",
       "         [ 0.70984317]]), matrix([[ 0.31250449],\n",
       "         [ 0.49462946],\n",
       "         [ 0.19476105],\n",
       "         [ 0.27654529]]), matrix([[-0.11505023],\n",
       "         [ 2.08096481],\n",
       "         [ 0.39876467],\n",
       "         [ 1.1190965 ]]), matrix([[ 0.3793898 ],\n",
       "         [ 0.26257475],\n",
       "         [ 0.1241411 ],\n",
       "         [ 0.14378113]]), matrix([[ 3.14087322],\n",
       "         [ 1.56604605],\n",
       "         [ 0.72965518],\n",
       "         [ 0.74400402]]), matrix([[ 0.37366593],\n",
       "         [ 0.32189252],\n",
       "         [ 0.10997431],\n",
       "         [ 0.1508832 ]]), matrix([[ 2.59759599],\n",
       "         [ 2.10855833],\n",
       "         [ 0.61485543],\n",
       "         [ 0.88518856]]), matrix([[ 0.25234976],\n",
       "         [ 0.42501962],\n",
       "         [ 0.09383318],\n",
       "         [ 0.17515559]]), matrix([[ 0.35971082],\n",
       "         [ 2.96710831],\n",
       "         [ 0.46433354],\n",
       "         [ 1.07018819]]), matrix([[ 0.3107487 ],\n",
       "         [ 0.14620663],\n",
       "         [ 0.03398101],\n",
       "         [ 0.06736861]]), matrix([[ 3.50067579],\n",
       "         [-0.82361528],\n",
       "         [-0.07343589],\n",
       "         [-0.20752779]]), matrix([[ 0.54329378],\n",
       "         [-0.10329381],\n",
       "         [-0.00715928],\n",
       "         [-0.02374457]])]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Initial Observation for Quaternion. \n",
    "Q_observation = np.matrix([[0.5], [0.5] , [0.5], [0.5]])\n",
    "testObj = GaussNewtonOptimization(Q1=float(Q_observation[0]),\n",
    "                                                     Q2=float(Q_observation[1]),\n",
    "                                                     Q3=float(Q_observation[2]),\n",
    "                                                     Q4=float(Q_observation[3]),\n",
    "                                                     Ax=data['Ax'][20],Ay=data['Ay'][20],Az=data['Az'][20],\n",
    "                                                     Mx=data['MFx'][20],My=data['MFy'][20],Mz=data['MFz'][20])\n",
    "testObj.Gaussnewton()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "Q_list = list()\n",
    "QMatrix_list = list()\n",
    "## Now we run the algorithm for all the values of the Accelerometer and Magnetometer. \n",
    "for i in range(len(data)):\n",
    "    Ax = data['Ax'][i]; Ay = data['Ay'][i]; Az = data['Az'][i]\n",
    "    Mx = data['MFx'][i]; My = data['MFy'][i]; Mz = data['MFz'][i]\n",
    "    testObj = GaussNewtonOptimization(Q1=float(Q_observation[0]),\n",
    "                                                     Q2=float(Q_observation[1]),\n",
    "                                                     Q3=float(Q_observation[2]),\n",
    "                                                     Q4=float(Q_observation[3]),\n",
    "                                                     Ax=Ax,Ay=Ay,Az=Az,\n",
    "                                                     Mx=Mx,My=My,Mz=Mz)\n",
    "    #print(\"The Q_Now\",testObj.Q_now)\n",
    "    #print(\"The Q_Next\",testObj.Gaussnewton())\n",
    "    #NormValue = GaussNewtonOptimization.norm(testObj.Gaussnewton())\n",
    "    #Q_observation = np.matrix([x/NormValue for x in testObj.Gaussnewton()]).transpose()\n",
    "    tempx = testObj.Gaussnewton()\n",
    "    QMatrix_list.append(tempx)\n",
    "    Q_observation = list([float(tempx[0]), float(tempx[1]), float(tempx[2]), float(tempx[3])])\n",
    "    Q_list.append(Q_observation)\n",
    "    #print(\"Next set of Q_observation(normalised :)\",Q_observation)\n",
    "    #print(\"-------------------------------------------------------------------------\")\n",
    "   \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "q0 = list();q1= list();q2= list();q3= list()\n",
    "\n",
    "# let us add it to data dataframe to make the plotting easier. \n",
    "for i in range(len(Q_list)):\n",
    "    q0.append(Q_list[i][0])\n",
    "    q1.append(Q_list[i][1])\n",
    "    q2.append(Q_list[i][2])\n",
    "    q3.append(Q_list[i][3])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/omkar/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "data[\"q0\"] = q0 ;data[\"q1\"] = q1 ;data[\"q2\"] = q2 ;data[\"q3\"] = q3  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Ax</th>\n",
       "      <th>Ay</th>\n",
       "      <th>Az</th>\n",
       "      <th>Gx</th>\n",
       "      <th>Gy</th>\n",
       "      <th>Gz</th>\n",
       "      <th>MFx</th>\n",
       "      <th>MFy</th>\n",
       "      <th>MFz</th>\n",
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       "      <th>q1</th>\n",
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       "      <td>0.084642</td>\n",
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       "      <td>9.691729</td>\n",
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       "      <td>0.450621</td>\n",
       "      <td>0.244311</td>\n",
       "      <td>0.137461</td>\n",
       "      <td>0.202629</td>\n",
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       "      <td>-0.079959</td>\n",
       "      <td>0.034413</td>\n",
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       "      <td>0.383072</td>\n",
       "      <td>-0.229843</td>\n",
       "      <td>9.730036</td>\n",
       "      <td>-0.002131</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>21.75</td>\n",
       "      <td>-39.25</td>\n",
       "      <td>0.697578</td>\n",
       "      <td>-0.700762</td>\n",
       "      <td>-0.092012</td>\n",
       "      <td>0.043831</td>\n",
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       "      <td>-0.731121</td>\n",
       "      <td>-0.111389</td>\n",
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       "      <td>-0.210690</td>\n",
       "      <td>9.615114</td>\n",
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       "      <td>-0.118226</td>\n",
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      "text/plain": [
       "       Timestamp        Ax        Ay        Az        Gx        Gy        Gz  \\\n",
       "0  1494274993079  0.421380 -0.191536  9.672575  0.000000  0.000000  0.000000   \n",
       "1  1494274993101  0.402226 -0.191536  9.653421  0.002131  0.002131  0.003196   \n",
       "2  1494274993126  0.402226 -0.172383  9.691729 -0.002131  0.001065  0.002131   \n",
       "3  1494274993147  0.383072 -0.229843  9.653421  0.002131  0.002131  0.002131   \n",
       "4  1494274993169  0.383072 -0.229843  9.730036 -0.002131  0.002131  0.001065   \n",
       "5  1494274993191  0.383072 -0.210690  9.653421  0.002131  0.004261  0.002131   \n",
       "6  1494274993218  0.402226 -0.210690  9.615114  0.001065  0.002131  0.001065   \n",
       "7  1494274993240  0.402226 -0.191536  9.691729 -0.005326 -0.003196  0.001065   \n",
       "\n",
       "    MFx    MFy    MFz        q0        q1        q2        q3  \n",
       "0 -3.25  22.00 -39.25 -1.290481  1.787433  0.005535  0.676066  \n",
       "1 -4.00  22.25 -38.75 -1.295359  1.582821  0.084642  0.265368  \n",
       "2 -4.00  22.25 -38.75  0.450621  0.244311  0.137461  0.202629  \n",
       "3 -4.00  22.25 -38.75  0.737718 -0.750252 -0.079959  0.034413  \n",
       "4 -4.00  21.75 -39.25  0.697578 -0.700762 -0.092012  0.043831  \n",
       "5 -4.00  21.75 -39.25  0.738904 -0.731121 -0.111389  0.057634  \n",
       "6 -4.00  22.25 -39.75  0.711181 -0.691054 -0.118226  0.065287  \n",
       "7 -4.00  22.25 -39.75  0.743498 -0.711908 -0.133638  0.077000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff07a27ac88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AJiIiIiIim1Ymk2Hfvn0bnYwNo0siRURERERENikFbCIiIiIiIpuUAjYREREREZFNSgGb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\niOQoTdhERERERERy1P8Dy3iA1II9v0wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff07a22aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n0tDcQHVTte83bHN6zyElMoX+Sf1ZVbyKfkn9GJk2kvOyz6PWWUtUSBQze8xk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\nwwZqhmEYhmEYhmEY04z/DzyNMvWr+OGIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff074c19ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uN0Taxws/kLZx5WvSWQisJ6OGvqpqYzudLPVLcIjUmY1ayzFlRst7d++Wtqeq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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff074bc3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Timestamp',y=['q0','q1','q2','q3'],title=\"Quaternion\",figsize=(15.0, 4.0),ylim=(-2,6))\n",
    "data.plot(x='Timestamp',y=['Ax','Ay','Az'],title=\"Acceleromter Training Raw Data\",figsize=(15.0, 4.0))\n",
    "#data.plot(x='Timestamp',y=['Gx','Gy','Gz'],title=\"Gyroscope Training Raw Data\",figsize=(15.0, 4.0))\n",
    "data.plot(x='Timestamp',y=['MFx','MFy','MFz'],title=\"Magnetometer Training Raw Data\",figsize=(15.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Quaternion Output\n",
    "\n",
    "Hence we get a decent output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t1 = GaussNewtonOptimization(0.5,0.5,0.5,0.5,1.2,0.65,8.5,2.3,1.5,2.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method GaussNewtonOptimization.Gaussnewton of <__main__.GaussNewtonOptimization object at 0x7ff074c19fd0>>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.Gaussnewton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        Gauss Newton Algorithm : returns the Quaternion derived from Accelerometer and Magnetometer,\n",
      "        iterated 10 times. \n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print (t1.Gaussnewton.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.81390061081109089,\n",
       " -0.1354800591525446,\n",
       " 0.36951932893885192,\n",
       " 0.82181091516987204)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.Gaussnewton()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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